Use the Central Limit Theorem to find the indicated probability. The sample size is n, the population proportion is p, and the sample proportion is .
n = 160, p = 0.29; P( > 0.3)

P(X > 0.3) = 0.3897

Step-by-step explanation:

Normal probability distribution:

Problems of normally distributed samples are solved using the z-score formula.

In a set with mean and standard deviation , the zscore of a measure X is given by:

The Z-score measures how many standard deviations the measure is from the mean. After finding the Z-score, we look at the z-score table and find the p-value associated with this z-score. This p-value is the probability that the value of the measure is smaller than X, that is, the percentile of X. Subtracting 1 by the pvalue, we get the probability that the value of the measure is greater than X.

Central limit theorem:

The Central Limit Theorem estabilishes that, for a random variable X, with mean and standard deviation , the sample means with size n of at least 30 can be approximated to a normal distribution with mean and standard deviation

Applying the central limit theorem:

P( > 0.3)

This is 1 subtracted by the pvalue of Z when X = 0.3. So

By the Central Limit Theorem

has a pvalue of 0.6103

1 - 0.6103 = 0.3897

So

P(X > 0.3) = 0.3897

x = 17

step-by-step explanation:

1st step: isolate x.

24 - 3x = -27

-24         -24

-3x   =   -51

2nd step: divide.

-3x     =   -51

  3             3

  x       =     17

hope this

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step-by-step explanation: